exa-contents vs llm-router

Two General Claude Code skills, side by side. Pick the right skill for your workflow with a side-by-side look at metadata, sample code, and install commands.

Side-by-side

Tag overlap

Sample code from each SKILL.md

curl -sS -X POST "https://api.exa.ai/contents" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '{
    "urls": ["https://example.com"],
    "text": true
  }'

flowchart TD
  A{Task type?} -->|Classify / validate / format / extract| T1["Tier 1: Haiku, GPT-4o-mini (~$0.001)"]
  A -->|Write / implement / review / synthesize| T2["Tier 2: Sonnet, GPT-4o (~$0.01)"]
  A -->|Reason / architect / judge / decompose| T3["Tier 3: Opus, o1 (~$0.10)"]
  
  T1 --> Q1{Quality sufficient?}
  Q1 -->|Yes| Done1[Use cheap model]
  Q1 -->|No| T2
  
  T2 --> Q2{Quality sufficient?}
  Q2 -->|Yes| Done2[Use balanced model]
  Q2 -->|No| T3

When to choose each

exa-contents — Call Exa Contents (POST /contents) for LLM-ready extraction from known URLs: text, highlights, summaries, links, image links, subpages, freshness-controlled crawl. Use when the agent already has URLs. Prefer Exa MCP…

llm-router — Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements. Routes cheap tasks to Haiku/GPT-4o-mini and complex tasks to Sonnet/Opus/o1. Use when deciding…

Both are free to install. If you're unsure, install both — Claude Code skills are isolated by filename and only collide if their trigger phrases overlap (rare). The richest signal is the SKILL.md body itself — open both skill pages and read the first paragraph of each.

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